Modern inventory systems, such as those in mail order warehouses, supply chain distribution centers, airport luggage systems, and custom-order manufacturing facilities, face significant challenges in responding to requests for inventory items. As inventory systems grow, the challenges of simultaneously completing a large number of packing, storing, and other inventory-related tasks become non-trivial.
In many inventory systems, an incoming inventory item is typically stored into an inventory bin so as to be quickly retrievable in response to an order for the inventory item. An inventory management system typically stores the identification and location of the inventory bin in which the inventory item is stored for use in locating and processing the inventory item in response to an order for the inventory item. For example, an inventory system worker can pick up the incoming inventory item and place the inventory item into the inventory bin. To keep track of where the inventory item is stored, it is important to efficiently and accurately identify the inventory bin into which the inventory item is placed. Existing approaches for keeping track of where inventory items are stored, however, may require the inventory system worker to perform time consuming acts beyond placing the inventory item into an inventory bin and retrieving the inventory item from the inventory bin, such as pushing a button associated with the inventory bin or scanning a barcode associated with the inventory bin. And while the inventory system worker may be required to perform less time consuming tasks when a computer vision system is used to track placement of the inventory item, such a computer vision system may be computationally intensive and expensive. Accordingly, improved approaches for keeping track of where an inventory item is stored are of interest.